



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__A							       =		logging.get_logger(__name__)

__A							       =		'▁'

__A							       =		{'vocab_file': 'sentencepiece.bpe.model'}

__A							       =		{
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

__A							       =		{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
__A							       =		['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']






class 						lowercase		(					_A):






	"""simple docstring"""


	a__ :   int						  =				VOCAB_FILES_NAMES
	a__ :   Optional[int]						  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a__ :   List[Any]						  =				PRETRAINED_VOCAB_FILES_MAP
	a__ :   Optional[int]						  =				["input_ids", "attention_mask"]

	a__ :   Optional[int]						  =				[]
	a__ :   List[str]						  =				[]




	def __init__(      self      :		Optional[int]		,    __UpperCAmelCase      :		int		,    __UpperCAmelCase      :		List[Any]="<s>"		,    __UpperCAmelCase      :		List[str]="</s>"		,    __UpperCAmelCase      :		int="</s>"		,    __UpperCAmelCase      :		Dict="<s>"		,    __UpperCAmelCase      :		Optional[int]="<unk>"		,    __UpperCAmelCase      :		Optional[Any]="<pad>"		,    __UpperCAmelCase      :		Optional[Any]="<mask>"		,    __UpperCAmelCase      :		Tuple=None		,    __UpperCAmelCase      :		int=None		,    __UpperCAmelCase      :		Union[str, Any]=None		,    __UpperCAmelCase      :		Optional[Dict[str, Any]] = None		,    __UpperCAmelCase      :		Union[str, Any]=None		,    **__UpperCAmelCase      :		List[Any]		,    )     ->					Optional[Any]:
							UpperCAmelCase_=  AddedToken(UpperCamelCase__		,    lstrip=UpperCamelCase__		,    rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__		,    UpperCamelCase__	) else mask_token

							UpperCAmelCase_=  {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=UpperCamelCase__		,    eos_token=UpperCamelCase__		,    unk_token=UpperCamelCase__		,    sep_token=UpperCamelCase__		,    cls_token=UpperCamelCase__		,    pad_token=UpperCamelCase__		,    mask_token=UpperCamelCase__		,    tokenizer_file=UpperCamelCase__		,    src_lang=UpperCamelCase__		,    tgt_lang=UpperCamelCase__		,    additional_special_tokens=UpperCamelCase__		,    sp_model_kwargs=self.sp_model_kwargs		,    **UpperCamelCase__		,    )

							UpperCAmelCase_=  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
							self.sp_model.Load(str(UpperCamelCase__	)	)
							UpperCAmelCase_=  vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							UpperCAmelCase_=  {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							UpperCAmelCase_=  1

							UpperCAmelCase_=  len(self.sp_model	)
							UpperCAmelCase_=  {
							    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase__	)
							}
							UpperCAmelCase_=  {v: k for k, v in self.lang_code_to_id.items()}
							UpperCAmelCase_=  len(self.sp_model	) + len(self.lang_code_to_id	) + self.fairseq_offset

							self.fairseq_tokens_to_ids.update(self.lang_code_to_id	)
							UpperCAmelCase_=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
							UpperCAmelCase_=  list(self.lang_code_to_id.keys()	)

							if additional_special_tokens is not None:
													# Only add those special tokens if they are not already there.
													self._additional_special_tokens.extend(
													    [t for t in additional_special_tokens if t not in self._additional_special_tokens]	)

							UpperCAmelCase_=  src_lang if src_lang is not None else """en_XX"""
							UpperCAmelCase_=  self.lang_code_to_id[self._src_lang]
							UpperCAmelCase_=  tgt_lang
							self.set_src_lang_special_tokens(self._src_lang	)




	def __getstate__(      self      :		Union[str, Any]	)     ->					List[Any]:
							UpperCAmelCase_=  self.__dict__.copy()
							UpperCAmelCase_=  None
							UpperCAmelCase_=  self.sp_model.serialized_model_proto()
							return state




	def __setstate__(      self      :		Optional[int]		,    __UpperCAmelCase      :		List[str]	)     ->					Dict:
							UpperCAmelCase_=  d

							# for backward compatibility
							if not hasattr(self		,    """sp_model_kwargs"""	):
													UpperCAmelCase_=  {}

							UpperCAmelCase_=  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)




	@property
	def     _SCREAMING_SNAKE_CASE						(      self      :		List[Any]	)     ->					List[str]:
							return len(self.sp_model	) + len(self.lang_code_to_id	) + self.fairseq_offset + 1  # Plus 1 for the mask token




	@property
	def     _SCREAMING_SNAKE_CASE						(      self      :		Tuple	)     ->					str:
							return self._src_lang




	@src_lang.setter
	def     _SCREAMING_SNAKE_CASE						(      self      :		Optional[Any]		,    __UpperCAmelCase      :		str	)     ->					None:
							UpperCAmelCase_=  new_src_lang
							self.set_src_lang_special_tokens(self._src_lang	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		Union[str, Any]		,    __UpperCAmelCase      :		List[int]		,    __UpperCAmelCase      :		Optional[List[int]] = None		,    __UpperCAmelCase      :		bool = False	)     ->					List[int]:






							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=UpperCamelCase__		,    token_ids_a=UpperCamelCase__		,    already_has_special_tokens=UpperCamelCase__	)

							UpperCAmelCase_=  [1] * len(self.prefix_tokens	)
							UpperCAmelCase_=  [1] * len(self.suffix_tokens	)
							if token_ids_a is None:
													return prefix_ones + ([0] * len(UpperCamelCase__	)) + suffix_ones
							return prefix_ones + ([0] * len(UpperCamelCase__	)) + ([0] * len(UpperCamelCase__	)) + suffix_ones




	def     _SCREAMING_SNAKE_CASE						(      self      :		Optional[int]		,    __UpperCAmelCase      :		List[int]		,    __UpperCAmelCase      :		Optional[List[int]] = None	)     ->					List[int]:






							if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




	def     _SCREAMING_SNAKE_CASE						(      self      :		Any		,    __UpperCAmelCase      :		List[int]		,    __UpperCAmelCase      :		Optional[List[int]] = None	)     ->					List[int]:
							UpperCAmelCase_=  [self.sep_token_id]
							UpperCAmelCase_=  [self.cls_token_id]

							if token_ids_a is None:
													return len(cls + token_ids_a + sep	) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]




	def     _SCREAMING_SNAKE_CASE						(      self      :		Optional[Any]		,    __UpperCAmelCase      :		Union[str, Any]		,    __UpperCAmelCase      :		str		,    __UpperCAmelCase      :		Optional[str]		,    __UpperCAmelCase      :		Optional[str]		,    **__UpperCAmelCase      :		Union[str, Any]	)     ->					int:






							if src_lang is None or tgt_lang is None:
													raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""	)
							UpperCAmelCase_=  src_lang
							UpperCAmelCase_=  self(UpperCamelCase__		,    add_special_tokens=UpperCamelCase__		,    return_tensors=UpperCamelCase__		,    **UpperCamelCase__	)
							UpperCAmelCase_=  self.convert_tokens_to_ids(UpperCamelCase__	)
							UpperCAmelCase_=  tgt_lang_id
							return inputs




	def     _SCREAMING_SNAKE_CASE						(      self      :		Any	)     ->					Tuple:
							UpperCAmelCase_=  {self.convert_ids_to_tokens(UpperCamelCase__	): i for i in range(self.vocab_size	)}
							vocab.update(self.added_tokens_encoder	)
							return vocab




	def     _SCREAMING_SNAKE_CASE						(      self      :		Optional[Any]		,    __UpperCAmelCase      :		str	)     ->					List[str]:
							return self.sp_model.encode(UpperCamelCase__		,    out_type=UpperCamelCase__	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		Dict		,    __UpperCAmelCase      :		Optional[int]	)     ->					Optional[int]:






							if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
							UpperCAmelCase_=  self.sp_model.PieceToId(UpperCamelCase__	)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




	def     _SCREAMING_SNAKE_CASE						(      self      :		List[Any]		,    __UpperCAmelCase      :		Optional[Any]	)     ->					Any:






							if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		Any		,    __UpperCAmelCase      :		List[str]	)     ->					Dict:
							UpperCAmelCase_=  """""".join(UpperCamelCase__	).replace(UpperCamelCase__		,    """ """	).strip()
							return out_string




	def     _SCREAMING_SNAKE_CASE						(      self      :		Union[str, Any]		,    __UpperCAmelCase      :		str		,    __UpperCAmelCase      :		Optional[str] = None	)     ->					Tuple[str]:






							if not os.path.isdir(UpperCamelCase__	):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
													return
							UpperCAmelCase_=  os.path.join(
							    UpperCamelCase__		,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

							if os.path.abspath(self.vocab_file	) != os.path.abspath(UpperCamelCase__	) and os.path.isfile(self.vocab_file	):
													copyfile(self.vocab_file		,    UpperCamelCase__	)
							elif not os.path.isfile(self.vocab_file	):
													with open(UpperCamelCase__		,    """wb"""	) as fi:
																			UpperCAmelCase_=  self.sp_model.serialized_model_proto()
																			fi.write(UpperCamelCase__	)

							return (out_vocab_file,)




	def     _SCREAMING_SNAKE_CASE						(      self      :		int		,    __UpperCAmelCase      :		List[str]		,    __UpperCAmelCase      :		str = "en_XX"		,    __UpperCAmelCase      :		Optional[List[str]] = None		,    __UpperCAmelCase      :		str = "ro_RO"		,    **__UpperCAmelCase      :		List[Any]		,    )     ->					BatchEncoding:
							UpperCAmelCase_=  src_lang
							UpperCAmelCase_=  tgt_lang
							return super().prepare_seqaseq_batch(UpperCamelCase__		,    UpperCamelCase__		,    **UpperCamelCase__	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		Any	)     ->					str:
							return self.set_src_lang_special_tokens(self.src_lang	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		List[Any]	)     ->					List[str]:
							return self.set_tgt_lang_special_tokens(self.tgt_lang	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		Any		,    __UpperCAmelCase      :		Any	)     ->					None:
							UpperCAmelCase_=  self.lang_code_to_id[src_lang]
							UpperCAmelCase_=  []
							UpperCAmelCase_=  [self.eos_token_id, self.cur_lang_code]




	def     _SCREAMING_SNAKE_CASE						(      self      :		str		,    __UpperCAmelCase      :		str	)     ->					None:
							UpperCAmelCase_=  self.lang_code_to_id[lang]
							UpperCAmelCase_=  []
							UpperCAmelCase_=  [self.eos_token_id, self.cur_lang_code]






import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Optional[Any]=0.01     ,				UpperCamelCase__     :					Tuple=1000       )     ->      str:







    """simple docstring"""






    __magic_name__								=			p_stop
    __magic_name__								=			max_length





  def __iter__(					self     :					Union[str, Any]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			0
    __magic_name__								=			False
    while not stop and count < self.max_length:
      yield count
      count += 1
      __magic_name__								=			random.random() < self.p_stop







class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					str=False     ,				UpperCamelCase__     :					Optional[Any]=True       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			[
        BatchSamplerShard(UpperCamelCase__     ,				2     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )
        for i in range(2       )
    ]
    __magic_name__								=			[list(UpperCamelCase__       ) for batch_sampler_shard in batch_sampler_shards]
    if not split_batches:
      self.assertListEqual([len(UpperCamelCase__       ) for shard in batch_sampler_shards]     ,				[len(UpperCamelCase__       ) for e in expected]       )
    self.assertListEqual(UpperCamelCase__     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					Any       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    # Expected shouldn't change
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    # Check the shards when the dataset is a round multiple of batch size but not total batch size.
    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
    # num_processes batch.
    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
    # num_processes batch.
    __magic_name__								=			BatchSampler(range(20       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(20       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    # Check the shards when the dataset is very small.
    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[[0, 1, 0]], [[1, 0, 1]]]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					Tuple       )     ->      int:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    # Expected shouldn't change
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size.
    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size or num_processes.
    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    # Check the shards when the dataset is very small.
    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[[0, 1]], [[0, 1]]]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
  def 				_lowercase				(					self     :					int       )     ->      str:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    # Expected shouldn't change
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is a round multiple of batch size but not total batch size.
    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
    # num_processes batch.
    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
    # num_processes batch.
    __magic_name__								=			BatchSampler(range(20       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(20       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
        [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is very small.
    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[[0, 1]], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=3     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       )
  def 				_lowercase				(					self     :					Optional[int]       )     ->      int:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(24       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    # Expected shouldn't change
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size.
    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(22       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is not a round multiple of batch size or num_processes.
    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(21       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[
        [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
        [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
    ]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    # Check the shards when the dataset is very small.
    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[[0, 1]], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )

    __magic_name__								=			BatchSampler(range(2       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			[[], []]
    self.check_batch_sampler_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				even_batches=UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict       )     ->      str:







    """simple docstring"""






    __magic_name__								=			[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
    __magic_name__								=			[BatchSamplerShard(UpperCamelCase__     ,				2     ,				UpperCamelCase__     ,				even_batches=UpperCamelCase__       ) for i in range(2       )]

    self.assertEqual(len(batch_sampler_shards[0]       )     ,				3       )
    self.assertEqual(len(batch_sampler_shards[1]       )     ,				2       )

    self.assertListEqual(list(batch_sampler_shards[0]       )     ,				[[0, 1, 2], [5, 6, 7, 8], [12, 13]]       )
    self.assertListEqual(list(batch_sampler_shards[1]       )     ,				[[3, 4], [9, 10, 11]]       )
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Dict=False     ,				UpperCamelCase__     :					Union[str, Any]=2     ,				UpperCamelCase__     :					Union[str, Any]=False       )     ->      Union[str, Any]:







    """simple docstring"""






    random.seed(UpperCamelCase__       )
    __magic_name__								=			list(UpperCamelCase__       )

    __magic_name__								=			[
        IterableDatasetShard(
            UpperCamelCase__     ,				batch_size=UpperCamelCase__     ,				drop_last=UpperCamelCase__     ,				num_processes=UpperCamelCase__     ,				process_index=UpperCamelCase__     ,				split_batches=UpperCamelCase__     ,				)
        for i in range(UpperCamelCase__       )
    ]
    __magic_name__								=			[]
    for iterable_dataset_shard in iterable_dataset_shards:
      # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
      random.seed(UpperCamelCase__       )
      iterable_dataset_lists.append(list(UpperCamelCase__       )       )

    __magic_name__								=			batch_size // num_processes if split_batches else batch_size
    # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
    __magic_name__								=			iterable_dataset_lists[0]
    for l in iterable_dataset_lists[1:]:
      self.assertEqual(len(UpperCamelCase__       )     ,				len(UpperCamelCase__       )       )
      self.assertTrue(len(UpperCamelCase__       ) % shard_batch_size == 0       )

    __magic_name__								=			[]
    for idx in range(0     ,				len(UpperCamelCase__       )     ,				UpperCamelCase__       ):
      for l in iterable_dataset_lists:
        observed += l[idx : idx + shard_batch_size]

    if not drop_last:
      while len(UpperCamelCase__       ) < len(UpperCamelCase__       ):
        reference += reference
    self.assertListEqual(UpperCamelCase__     ,				reference[: len(UpperCamelCase__       )]       )
  def 				_lowercase				(					self     :					Tuple       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			42
    __magic_name__								=			RandomIterableDataset()

    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )

    # Edge case with a very small dataset
    __magic_name__								=			RandomIterableDataset(max_length=2       )

    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
    self.check_iterable_dataset_shards(UpperCamelCase__     ,				UpperCamelCase__     ,				batch_size=4     ,				drop_last=UpperCamelCase__     ,				split_batches=UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[Any]       )     ->      int:







    """simple docstring"""






    __magic_name__								=			BatchSampler(range(16       )     ,				batch_size=4     ,				drop_last=UpperCamelCase__       )
    __magic_name__								=			SkipBatchSampler(UpperCamelCase__     ,				2       )
    self.assertListEqual(list(UpperCamelCase__       )     ,				[[8, 9, 10, 11], [12, 13, 14, 15]]       )
  def 				_lowercase				(					self     :					Tuple       )     ->      str:







    """simple docstring"""






    __magic_name__								=			SkipDataLoader(list(range(16       )       )     ,				batch_size=4     ,				skip_batches=2       )
    self.assertListEqual([t.tolist() for t in dataloader]     ,				[[8, 9, 10, 11], [12, 13, 14, 15]]       )
  def 				_lowercase				(					self     :					str       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			DataLoader(list(range(16       )       )     ,				batch_size=4       )
    __magic_name__								=			skip_first_batches(UpperCamelCase__     ,				num_batches=2       )
    self.assertListEqual([t.tolist() for t in new_dataloader]     ,				[[8, 9, 10, 11], [12, 13, 14, 15]]       )
  def 				_lowercase				(					self     :					List[str]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			DataLoaderShard(list(range(16       )       )     ,				batch_size=4       )
    for idx, _ in enumerate(UpperCamelCase__       ):
      self.assertEqual(dataloader.end_of_dataloader     ,				idx == 3       )

    # Test it also works on the second iteration
    for idx, _ in enumerate(UpperCamelCase__       ):
      self.assertEqual(dataloader.end_of_dataloader     ,				idx == 3       )





  def 				_lowercase				(					self     :					Tuple       )     ->      List[str]:







    """simple docstring"""






    Accelerator()
    __magic_name__								=			DataLoaderDispatcher(range(16       )     ,				batch_size=4       )
    for idx, _ in enumerate(UpperCamelCase__       ):
      self.assertEqual(dataloader.end_of_dataloader     ,				idx == 3       )

    # Test it also works on the second iteration
    for idx, _ in enumerate(UpperCamelCase__       ):
      self.assertEqual(dataloader.end_of_dataloader     ,				idx == 3       )

